source for csinva.io
slides •
research overviews •
cheat sheets •
notes
posts •
research links •
personal info
@csinva_
pres folder contains source for presentations, including ml slides from teaching machine learning at berkeley - the source is in markdown (built with reveal-md) and is easily editable / exportable
overviews and summaries of recent papers in different research areas in the research_ovws folder (e.g. ml interpretability, theory, complexity, scattering transform, dl in neuroscience)
the _notes folder contains markdown notes and cheat-sheets for many different courses and areas between computer science, statistics, and neuroscience
links to research code, such as these repos:
| interpretable machine learning | interpretable deep learning | deep learning fun |
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| imodels: transparent model fitting, DAC: disentangled attribution curves | ACD: hierarchical interpretations, TRIM: interpreting transformations, CDEP: penalizing explanations | GAN/VAE: demo models, paper-title generator with gpt2 |
posts on various aspects of machine learning / statistics / neuroscience advancements
| interpretability | connectomics | disentanglement |
|---|---|---|
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- for updates, star the repo or follow @csinva_
- feel free to use openly!
- built with jekyll + github pages
- uses timeline theme and particles.js